I want to merge columns based on same IDs and want to make sure to consolidate the rows into just one row (per ID). Can anyone help me to merge the columns for duplicates and non-duplicates?
Given:
ID Name Degree AM_Class PM_Class Online_Class
01 Kathy Biology Bio101 NaN NaN
01 Kathy Biology NaN Chem101 NaN
02 James Chemistry NaN Chem101 NaN
03 Henry Business Bus100 NaN NaN
03 Henry Business NaN Math100 NaN
03 Henry Business NaN NaN Acct100
Expected Output:
ID Name Degree AM_Class PM_Class Online_Class
01 Kathy Biology Bio101 Chem101 NaN
02 James Chemistry NaN Chem101 NaN
03 Henry Business Bus100 Math100 Acct100
I tried to use:
df = df.groupby(['Name','Degree','ID'])['AM_Class', 'PM_Class', 'Online_Class'].apply(', '.join).reset_index()
but seems like it's giving an error..
Here is your data:
df = pd.DataFrame({'ID': ['01', '01', '02', '03', '03', '03'],
'Degree': ['Biology', 'Biology', 'Chemistry', 'Business', 'Business', 'Business'],
'Name': ['Kathy', 'Kathy', 'James', 'Henry', 'Henry', 'Henry'],
'AM_Class': ['Bio101', np.nan, np.nan, 'Bus100', np.nan, np.nan],
'PM_Class': [np.nan, 'Chem101', 'Chem101', np.nan, 'Math100', np.nan],
'Online_Class': [np.nan, np.nan, np.nan, np.nan, np.nan, 'Acct100']})
You can separate the data frames, remove the NaN values, then rejoin them.
The reduce() function allows the merge to be performed iteratively, without having to merge the data frames one by one.
from functools import reduce
# Separate the data frames
df_student = df[['ID', 'Name', 'Degree']]
df_AM = df[['ID', 'Name', 'AM_Class']]
df_PM = df[['ID', 'Name', 'PM_Class']]
df_OL = df[['ID', 'Name', 'Online_Class']]
# List of data frames
dfs = [df_student, df_AM, df_PM, df_OL]
# Remove all NaNs
for df in dfs:
df.dropna(inplace=True)
# Merge dataframes without the NaNs
df_merged = reduce(lambda left, right: pd.merge(left, right, how='left', on=['ID', 'Name']), dfs)
ID Name Degree AM_Class PM_Class Online_Class
0 01 Kathy Biology Bio101 Chem101 NaN
1 01 Kathy Biology Bio101 Chem101 NaN
2 02 James Chemistry NaN Chem101 NaN
3 03 Henry Business Bus100 Math100 Acct100
4 03 Henry Business Bus100 Math100 Acct100
5 03 Henry Business Bus100 Math100 Acct100
Then you just need to remove the duplicates.
df_merged.drop_duplicates(inplace=True).reset_index()
This is the result:
ID Name Degree AM_Class PM_Class Online_Class
0 01 Kathy Biology Bio101 Chem101 NaN
1 02 James Chemistry NaN Chem101 NaN
2 03 Henry Business Bus100 Math100 Acct100
You may ffill
rows first and then drop duplicates while keeping the last occurrence of duplicates,
df.groupby(['ID']).ffill().drop_duplicates(subset='Name', keep='last')
we can use pandas pivot_table for this problem your data looks like this
>>> data = {'Name': ['Kathy','Kathy','James','Henry','Henry','Henry'],
'Degree': ['Biology','Biology','Chemistry','Business','Business','Business'],
'AM_Class': ['Bio101', np.nan, np.nan, 'Bus100', np.nan, np.nan],
'PM_Class': [np.nan, 'Chem101', 'Chem101', np.nan, 'Math100', np.nan],
'Online_Class': [np.nan, np.nan, np.nan, np.nan, np.nan, 'Acct100'],
}
>>> df = pd.DataFrame(data)
>>> print(df)
Name Degree AM_Class PM_Class Online_Class
0 Kathy Biology Bio101 NaN NaN
1 Kathy Biology NaN Chem101 NaN
2 James Chemistry NaN Chem101 NaN
3 Henry Business Bus100 NaN NaN
4 Henry Business NaN Math100 NaN
5 Henry Business NaN NaN Acct100
First we can replace all NaN
with null string
>>> df.fillna('', inplace=True)
>>> print(df)
Name Degree AM_Class PM_Class Online_Class
0 0 Biology Bio101
1 1 Biology Chem101
2 2 Chemistry Chem101
3 3 Business Bus100
4 4 Business Math100
5 5 Business Acct100
I am doing this because while using pivot_table function I would like to use np.sum
function which will concatenate strings in the pandas.series. Having the np.nan
as it is will raise exception.
Now lets make the pivot table with Name
being the group-by column.
>>> df2 = pd.pivot_table(data=df, index=['Name'], aggfunc={'Degree':np.unique, 'AM_Class':np.sum, 'PM_Class':np.sum, 'Online_Class':np.sum})
>>> print(df2)
AM_Class Degree Online_Class PM_Class
Name
Henry Bus100 Business Acct100 Math100
James Chemistry Chem101
Kathy Bio101 Biology Chem101
We have to replace the nulls with np.nan - since that is the format that is asked for.
>>> df2.replace('', np.nan, inplace=True)
>>> print(df2)
AM_Class Degree Online_Class PM_Class
Name
Henry Bus100 Business Acct100 Math100
James NaN Chemistry NaN Chem101
Kathy Bio101 Biology NaN Chem101
Observing the new dataframe df2
, it seems we have to make the following changes
>>> df2['Name'] = df2.index
>>> cols = [ 'Name', 'Degree', 'AM_Class', 'PM_Class', 'Online_Class']
>>> df2 = df2[cols]
>>> print(df2)
Name Degree AM_Class PM_Class Online_Class
Name
Henry Henry Business Bus100 Math100 Acct100
James James Chemistry NaN Chem101 NaN
Kathy Kathy Biology Bio101 Chem101 NaN
>>> df2.set_index(pd.RangeIndex(start=0,stop=3,step=1), inplace=True)
>>> print(df2)
Name Degree AM_Class PM_Class Online_Class
0 Henry Business Bus100 Math100 Acct100
1 James Chemistry NaN Chem101 NaN
2 Kathy Biology Bio101 Chem101 NaN
If need first non missing values per groups use GroupBy.first
:
df = df.groupby(['ID','Name','Degree'], as_index=False).first()
print (df)
ID Name Degree AM_Class PM_Class Online_Class
0 01 Kathy Biology Bio101 Chem101 None
1 02 James Chemistry None Chem101 None
2 03 Henry Business Bus100 Math100 Acct100
Or if need all unique values without missing values per groups use custom lambda function in GroupBy.agg
for processing each column separately by Series.dropna
, removed duplicated by dict.fromkeys
and last join values by ,
:
f = lambda x: ', '.join(dict.fromkeys(x.dropna()))
df = df.groupby(['ID','Name','Degree'], as_index=False).agg(f).replace('', np.nan)
Difference is possible see in changed data:
print (df)
ID Name Degree AM_Class PM_Class Online_Class
0 01 Kathy Biology Bio101 NaN NaN
1 01 Kathy Biology NaN Chem101 NaN
2 02 James Chemistry NaN Chem101 NaN
3 03 Henry Business Bus100 NaN NaN
4 03 Henry Business NaN Math100 Acct100
5 03 Henry Business NaN Math200 Acct100
df1 = df.groupby(['ID','Name','Degree'], as_index=False).first()
print (df1)
ID Name Degree AM_Class PM_Class Online_Class
0 01 Kathy Biology Bio101 Chem101 None
1 02 James Chemistry None Chem101 None
2 03 Henry Business Bus100 Math100 Acct100
f = lambda x: ', '.join(dict.fromkeys(x.dropna()))
df2 = df.groupby(['ID','Name','Degree'], as_index=False).agg(f).replace('', np.nan)
print (df2)
ID Name Degree AM_Class PM_Class Online_Class
0 01 Kathy Biology Bio101 Chem101 NaN
1 02 James Chemistry NaN Chem101 NaN
2 03 Henry Business Bus100 Math100, Math200 Acct100
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